The Role

Designs and delivers scalable batch and streaming data solutions on Azure and Microsoft Fabric. Builds lakehouse, warehouse, and semantic layers for analytics and AI; implements data quality, metadata, lineage, monitoring, security, and CI/CD. Owns technical workstreams from design through production, optimizes performance and cost, manages stakeholders, reviews designs, establishes engineering standards, and mentors data engineers.

Summary Generated by Built In

Role overview: Data is the fuel for every AI solution we build — and we're looking for someone who can own that foundation end-to-end. As a Senior Data Engineer, you'll design and deliver scalable cloud data solutions on Microsoft Azure and Fabric, combining hands-on engineering with real technical leadership: owning solution design, setting the standards your team works to, and mentoring the engineers around you.

Key responsibilities:

• Design and develop batch and streaming data pipelines using Azure Data Factory, Fabric Data Factory, notebooks and related services.
• Build curated lakehouse, warehouse and semantic data layers that support analytics and AI use cases.
• Implement data-quality checks, metadata, lineage, monitoring, alerting and operational support.
• Optimise pipelines and storage for reliability, security, performance and cost.
• Apply source control, automated testing and CI/CD through Azure DevOps or GitHub.
• Own delivery of your workstreams end-to-end — from design through to production, managing risk and stakeholders along the way
• Set the bar for engineering quality — reviewing designs, shaping standards, and mentoring the team to raise the floor, not just the ceiling.

Qualifications, skills and experience:

• Significant data-engineering experience with SQL and Python or PySpark.
• Hands-on experience with Microsoft Fabric or Azure services such as Data Factory, Data Lake Storage, Synapse Analytics and Databricks.
• Knowledge of dimensional modelling, lakehouse patterns, medallion architecture and data integration.
• Experience with identity, access control, data protection and production support.
• Demonstrated ability to own technical workstreams and communicate design decisions clearly to both technical and non-technical audiences.

Desirable: Event Hubs, Kafka, Purview, Power BI semantic models, dbt and relevant Azure or Fabric certifications.

The Role

Designs and delivers scalable batch and streaming data solutions on Azure and Microsoft Fabric. Builds lakehouse, warehouse, and semantic layers for analytics and AI; implements data quality, metadata, lineage, monitoring, security, and CI/CD. Owns technical workstreams from design through production, optimizes performance and cost, manages stakeholders, reviews designs, establishes engineering standards, and mentors data engineers.

Summary Generated by Built In

Role overview: Data is the fuel for every AI solution we build — and we're looking for someone who can own that foundation end-to-end. As a Senior Data Engineer, you'll design and deliver scalable cloud data solutions on Microsoft Azure and Fabric, combining hands-on engineering with real technical leadership: owning solution design, setting the standards your team works to, and mentoring the engineers around you.

Key responsibilities:

• Design and develop batch and streaming data pipelines using Azure Data Factory, Fabric Data Factory, notebooks and related services.
• Build curated lakehouse, warehouse and semantic data layers that support analytics and AI use cases.
• Implement data-quality checks, metadata, lineage, monitoring, alerting and operational support.
• Optimise pipelines and storage for reliability, security, performance and cost.
• Apply source control, automated testing and CI/CD through Azure DevOps or GitHub.
• Own delivery of your workstreams end-to-end — from design through to production, managing risk and stakeholders along the way
• Set the bar for engineering quality — reviewing designs, shaping standards, and mentoring the team to raise the floor, not just the ceiling.

Qualifications, skills and experience:

• Significant data-engineering experience with SQL and Python or PySpark.
• Hands-on experience with Microsoft Fabric or Azure services such as Data Factory, Data Lake Storage, Synapse Analytics and Databricks.
• Knowledge of dimensional modelling, lakehouse patterns, medallion architecture and data integration.
• Experience with identity, access control, data protection and production support.
• Demonstrated ability to own technical workstreams and communicate design decisions clearly to both technical and non-technical audiences.

Desirable: Event Hubs, Kafka, Purview, Power BI semantic models, dbt and relevant Azure or Fabric certifications.